SIPL1 enhances the proliferation, attachment, and migration of CHO cells by inhibiting PTEN function
Bibliographic record
Abstract
The PTEN tumour suppressor plays critical roles in inhibiting cell proliferation, adhesion and migration through downregulation of the PI3K-AKT pathway. SIPL1 is a novel PTEN‑negative regulator (PTEN-NR) that contributes to PTEN inactivation during tumorigenesis. However, whether SIPL1 plays a role in inhibiting PTEN function in the process of cell adhesion and migration remains unclear. The aim of this study was to investigate this possibility using CHO-K1 cells, and western blotting, qPCR analyses and microscopy. Results showed that the overexpression of SIPL1 in CHO-K1 cells decreased the amount of PTEN protein. The downregulation was not caused by an obvious reduction in PTEN mRNA levels or ubiquitin-dependent protein degradation. Nonetheless, the reduction was functional, as SIPL1 overexpression increased the activation of AKT under serum‑starved conditions, promoting CHO-K1 cell proliferation in an AKT‑dependent manner. Furthermore, SIPL1 increased the migration and attachment of CHO-K1 cells. Taken together, the evidence suggested that SIPL1 promotes AKT activation by decreasing the amount of PTEN protein in CHO-K1 cells, thereby promoting cell proliferation and migration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".